Natural Language Processing

Chapter 1: Introduction to Natural Language Processing
This chapter introduces natural language processing and explains how machines understand and generate human language. Students learn key NLP applications such as chatbots, search engines, sentiment analysis, and language translation.

Chapter 2: Text Processing & Linguistic Fundamentals
Learners explore text preprocessing techniques including tokenization, stemming, lemmatization, stop-word removal, and basic linguistic concepts that prepare text for analysis.

Chapter 3: Text Representation & Feature Engineering
This chapter focuses on converting text into numerical form using techniques such as Bag of Words, TF-IDF, and word embeddings, enabling machines to process language data.

Chapter 4: Classical NLP Models & Algorithms
Students learn traditional NLP approaches including n-grams, probabilistic models, and basic machine learning algorithms used for text classification and analysis.

Chapter 5: Deep Learning for NLP
This chapter introduces neural network-based NLP models such as RNNs, LSTMs, and transformers, explaining how deep learning has advanced language understanding.

Chapter 6: Practical NLP Applications
Learners apply NLP techniques to real-world tasks such as sentiment analysis, text classification, named entity recognition, chatbots, and document summarization.

Chapter 7: NLP Ethics, Bias & Real-World Challenges
This chapter covers ethical considerations in NLP, including bias in language models, data privacy, responsible AI use, and challenges in deploying NLP systems at scale.

Chapter 8: Paid Internship Program (Internovate Exclusive)
The final chapter places students into a paid NLP internship where they work on real language-based projects, assist in model development or AI integrations, receive mentor feedback, and earn an internship certificate and experience letter, preparing them for careers in AI and data science.

Rs. 3999 Rs.1999

Duration: 3 Months